The interpretation of natural language location descriptions—a priceless source of geographic data—is the focus of the current study. Interpreting a location description involves matching the geographical entities found in the text with the spatial database (in this case, OSM). The understanding of paraphrased places—that is, entities for which a name is not given and can only be described—is the main focus of this work. Our objective is to produce entity types that are semantically associated so that the spatial database may be searched for the particular location. For instance, finding an eatery requires first searching for things in an unidentified category. Due to its open-endedness, ambiguity, context sensitivity, and incompatibilities with human intellect, language poses difficulties. This paper’s first contribution is the presentation of a challenging issue that is essential to geo- information retrieval beyond named entities. Second, we suggest using association rule mining and clustering to identify location categories in context-sensitive ways. We assess the techniques using material taken from travel blogs and Wikipedia, demonstrating how they have advanced automatic place description interpretation to locales that have been paraphrased.